Video Title: Anthropic’s hunt to find the next Claude Code. Video Author: ACCESS Podcast.
Editor’s Note: Against the backdrop of large-scale model capabilities leaping forward and AI programming tools rapidly becoming popular, industry discussions are shifting from “Can the model complete the task” to “How can model capabilities be organized into products, workflows, and business systems”.
Over the past year, products such as Claude Code, Codex, and Co-work have successively entered the developer and knowledge worker scenes. AI is no longer just a chat box that answers questions, but has begun to become a production interface that can be invoked as a tool, perform tasks, and verify results. However, as the consensus that “the agent will become the next generation of software form” gradually emerges, a more critical question begins to emerge: Who can first transform model capabilities into reusable, distributable, and scalable working systems?
This article is compiled from an interview of Mike Krieger by ACCESS Podcast. Mike Krieger, co-founder of Instagram and currently Chief Product Officer at Anthropic, is responsible for Anthropic Labs, which aims to lead the team in exploring the next batch of cutting-edge products after Claude Code.
In this conversation, Mike Krieger does not simply discuss what Anthropic’s next product will be, but breaks down AI product competition into a set of more foundational structural issues: how model capabilities enter real workflows, how AI companies organize innovation internally, how platform companies handle boundaries with ecological customers, and as AI execution capabilities become stronger, where human judgment will be repositioned in the production chain.
First, the product form transitions from “chat” to “task”. In the past, large models mainly existed in the form of dialog boxes, where the user input a prompt and the model generated a response. Now, products like Claude Code, Co-work, and Claude Design represent a different product logic: enabling AI to continuously advance work around a goal, and in the process, invoke tools, generate results, and perform verification. This means that the key to AI products is no longer just answer quality, but the ability to decompose tasks, maintain continuity of context, invoke tools, and verify results. Whoever can encapsulate these capabilities into a seamless workflow is closer to the next generation productivity gateway.
Second, the organizational approach has shifted from “Big Team Planning” to “Small Team Experimentation.” The operating style of Anthropic Labs is more like an entrepreneurial unit embedded within a large company: starting with two or three people, holding bi-weekly reviews, and using high-frequency feedback to determine whether the project should continue. In the past, innovation labs in large companies tended to fall into long cycles, unclear responsibilities, and projects deemed “good enough” being postponed. Now, the model has reduced the construction cost, and what is truly scarce is judgment, taste, and decision speed. This means that the organizational efficiency in the AI era depends not only on the number of engineering personnel but on whether a smaller team can more quickly validate the direction.
Third, the boundary between platforms and applications is being redefined. The success of Claude Code has transformed Anthropic from just a model provider to also actively shaping application forms. The controversy between Claude Design and Figma demonstrates that a model company venturing into applications will inevitably encroach on the interests of customers and ecosystem partners. In the past, basic model companies mostly provided underlying capabilities, with vertical applications such as Cursor and Figma handling user interfaces and scenario encapsulation. Now, model companies also need to showcase an agent-first future form through their own products. This means that AI platform competition is not only about API competition but also about product paradigm competition.
Fourth, the stronger AI becomes, the scarcer human judgment is. Mike has repeatedly emphasized that Claude can code faster, generate prototypes, and perform tasks more quickly, but it cannot replace the most difficult part of the 0 to 1 process: posing the right questions, understanding real users, defining the product’s North Star, and determining what is “right.” In the past, execution ability was the primary bottleneck of knowledge work. Now, execution is being accelerated by models, and human value is more focused on preliminary judgment, creativity, relationship networks, and organizational abilities. AI will not automatically eliminate difficult decisions but will instead magnify the impact of erroneous directions more quickly.
If this discussion were to be condensed into one judgment, it would be this: After Claude Code, Anthropic is not seeking a single blockbuster product but a set of methods that will transform AI from model capability into a production system. In this sense, the subject of this article is no longer just Anthropic’s next product roadmap but a structural turning point for the entire AI industry, transitioning from a “model competition” to a “system competition.”
[BlockBeats]
The AI Production System Revolution: Implications for Crypto Investors
As Anthropic transitions from Claude Code to a broader ecosystem of production AI systems, the entire industry is witnessing a fundamental shift from isolated model capabilities to integrated AI workflows. For crypto investors, this evolution represents both unprecedented opportunities and significant strategic challenges that could reshape the competitive landscape for years to come.
The Paradigm Shift: From Chat to Production Systems
The article correctly identifies that AI’s value proposition has fundamentally transformed. We’re no longer merely evaluating models based on their ability to generate text or code, but on their capacity to execute complex, multi-step workflows autonomously. This shift from conversational AI to production systems creates direct parallels with blockchain’s own evolution from simple transactions to complex DeFi protocols and DAOs.
For crypto investors, the most immediate implication is the rising importance of infrastructure that enables AI agents to securely interact with blockchain networks. Projects providing wallet abstraction, secure execution environments, and AI-to-smart contract integration are positioned to capture significant value as these production systems mature.
Platform vs. Application: The New Battleground
Anthropic’s expansion into application territory (exemplified by Claude Design’s competition with Figma) mirrors dynamics we’ve seen throughout Web3’s evolution. In crypto, we witnessed how infrastructure providers (like Ethereum) and application layer protocols (like Uniswap) developed complex, sometimes contentious, relationships. The same dynamic is emerging in AI.
For crypto investors, this creates both opportunities and risks:
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Opportunity: Projects that can position themselves as specialized AI-blockchain hybrids, focusing on vertical applications where blockchain properties (decentralization, trustlessness, provable execution) provide unique advantages, could thrive.
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Risk: As centralized AI giants develop their own production systems, they may leverage their dominance to marginalize smaller players, similar to how Web2 platforms have historically treated third-party developers.
The Scarcity of Human Judgment in an Automated World
Perhaps the most profound insight from the article is the observation that as AI handles execution tasks, human judgment becomes increasingly valuable. This directly informs investment theses in crypto governance and DAO structures:
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Governance Tokens: As AI systems automate execution, the value of governance mechanisms that can set parameters, define objectives, and oversee AI behavior will increase. Projects with robust governance systems may see outsized returns.
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Reputation Systems: In a world where AI agents manage financial operations, systems for verifying the trustworthiness and reliability of these agents become critical. Reputation-based crypto protocols could capture significant value.
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Hybrid Human-AI Systems: The most successful platforms may be those that effectively combine AI’s execution capabilities with human oversight—precisely the model emerging in decentralized autonomous organizations (DAOs).
Organizational Innovation and Crypto’s Competitive Edge
Anthropic’s adoption of small-team experimentation mirrors the organizational advantages traditionally associated with crypto-native projects. For crypto investors, this reinforces the value thesis for:
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Modular Protocols: Projects that enable composability and allow smaller teams to build specialized components on top of a shared infrastructure.
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Funding Mechanisms: Crypto-native funding models like venture studios, DAO treasuries, and tokenized incentives may prove more adaptive than traditional corporate structures in this rapidly evolving landscape.
Investment Opportunities in the AI-Production System Era
Based on these trends, crypto investors should prioritize:
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AI Agent Infrastructure: Projects providing secure execution environments, wallet management, and AI-to-blockchain interfaces stand to benefit from the shift toward production AI systems.
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Verification Oracles: As AI systems make more autonomous decisions, the need for reliable verification of their outputs becomes critical. Oracle solutions with specialized AI verification capabilities could see significant demand.
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Tokenized AI Marketplaces: Platforms that enable the tokenization of AI models and workflows, allowing for permissionless access and composability, could become essential infrastructure in the AI economy.
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AI-DeFi Integration Protocols: Projects that bridge AI decision-making with DeFi execution may capture value from both the AI productivity revolution and the continued growth of decentralized finance.
Strategic Considerations for Navigating the Transition
As the industry shifts from model competition to system competition, crypto investors should:
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Focus on Complementary Strengths: Rather than competing directly with centralized AI systems, prioritize technologies that provide unique advantages like decentralized identity, data provenance, and verifiable execution.
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Monitor Tension Points: The friction between AI platforms and application developers creates opportunities for middleware solutions that can facilitate interoperability and fair value distribution.
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Emphasize Trust Mechanisms: As AI systems become more powerful, the ability to verify their behavior and ensure alignment with human values becomes increasingly valuable—precisely where blockchain technologies can provide unique benefits.
The AI production system revolution is not just a technological evolution but a fundamental reorganization of how value is created and distributed. For crypto investors, this represents both a challenge to existing theses and an opportunity to shape the next generation of AI-blockchain infrastructure. The most successful projects will be those that recognize this shift and position themselves not merely as components in a larger system, but as essential enablers of the production systems themselves.